This captures the basic meaning. But there are some aspects of this definition I don’t like. First and most obviously, no one enters a keyword string into a search engine to fill downtime. Searchers are on a mission to answer a question. Filling downtime implies passive consumption, as in channel surfing or browsing. The mere fact that someone is searching for something is itself instructive of what they’re about. It’s part of their user intent.

And that is my larger point. User intent is more fundamental than search. It is part of all interactions initiated by clients or prospects involving the user asking a question. At every point in users’ journeys, when they need an answer to a question quickly, we can infer their user intent from the content of that experience. For example, if a user has a problem with a piece of software and needs to know how to work around it or fix it, she will ask for help. Suppose she engages in an in-app chat with a bot. The questions she asks in that chat will determine the chat bot’s responses. The bot is successful to the extent that it satisfies her user intent, or answers her questions in a way that leads to her success.

We focus on the search experience for user intent modeling because the data is readily available. Google is the largest open repository of user intent data on the planet. So the models have more validity. But the models don’t just apply to search experiences. They could apply to any digital experience in which a client or prospect asks a question. This could be Siri, or Amazon, or on your corporate intranet. If you could learn all your users’ questions, and build a database of answers, you could have a universal customer chat bot driven by user intent modeling.

What is user intent modeling?

A user intent model is a knowledge graph that relates queries to questions, and questions to answers. When you start to draw this graph, you will find it can get very complicated very quickly.

Answering questions is the easy part. The hard part is understanding what question users are asking when they don’t phrase their queries in the form of a question. In search experiences, this is common. For example, when users type “cloud computing” into a search engine, most of the time, they mean “what is cloud computing?” You could build a statistical model to say how likely that question matches that query. Based on my last study, in this example, it’s around 80%– a strong user intent model for that query.

An easy way to check the prevalent question related to a query is to search in Google. When you search on “cloud computing,” you get lots of results that have “what is cloud computing?” in the title or headings.

In general user intent modeling is not as certain as “cloud computing.” Often, there are multiple questions that could fit a query with about equal certainty. In those cases, your knowledge graph will have many-to-one or many-to-many relationships. How do you choose between two competing questions related to a single query? Context. The problem is, queries offer few contextual cues–those little facts about who searchers are, what a typical information journey looks like for folks like them, and where they are in their journeys. But you still can infer some important context from the query itself.

Typical user intents

One place to start is to identify typical user intents for your target audience. The Wikipedia definition above gave us two legitimate examples: Fact checking and comparison shopping. In fact checking, you often see lot of questions with the words like “who,” as in “who invented electricity;” and “did,” as in “Did Tesla invent electricity?”

Fact checking is a special case because almost all queries are in the form of a question. So it’s relatively easy. But what words do you look for when someone is comparison shopping? The most common contextual cue is the word “versus” or “vs.” Other words you commonly find in comparison queries include “best” and “cheapest.” If you see those words, you have a pretty good sense of what content best answers the question implicit in the query.

You might think, “how can I develop a catalog of all my customers questions?” Start by describing typical customers and modeling their buyer journeys. You don’t need to model all of their questions. You can throw out the ones that are not related to your buyer journeys. For example, you can exclude “cheapest” as a contextual cue for the comparison phase of your buyer journey. No one makes money selling the cheapest kind. And you can exclude fact checking altogether because arguing about facts is not typically part of a buyer journey.

In general, you can focus on the query/question pairs that align with your customer journeys, and ignore the rest. That narrows the challenge down considerably.

What, why, how framework

If you have a typical buyer journey, a user intent model can help you say, “these are the questions we want to align our content strategy around, and these are the common queries that relate to those questions.”

For example, in the Awareness phase, focus on “what is” questions. If you don’t think your customers ask these basic questions, future customers will. Even if they don’t need the answer now, it’s good to have a ready answer to refer to. In our research, “what is” queries have the highest volume. So a lot of people ask them. Why not build good enough answers to those questions, and weave them into your experiences? At the very least, it’s a great way to capture some of the traffic from people asking those questions.

The second most frequent question in the awareness phase is “why?” As in, “why should I migrate to the cloud?” Variants include, “what are the benefits of migrating to the cloud.” These all can be grouped into the why bucket. Every interested party has to juggle the relative importance of buying this one thing versus any number of other investments they could make.

Answering the why question is essential to any marketing campaign. But many marketers don’t want to do so explicitly. So they build strategic value propositions and bury them behind messaging. Building content that explicitly answers the why question is the easiest way to capture prospects. All it takes is repackaging what you already have in an explicit answer to the why question.

Further down the journey, we see a lot of how questions, e.g. “How do I migrate to the cloud?” There are all kinds of variants, but it is a word that we see often in query analysis. One of the most popular sites on the web is How Stuff Works, and for good reason. Buyers need to know how this new thing they might buy will integrate into their existing systems and improve their bottom line in the process.

Marketers are not good at answering how questions. But every company in business has a help desk or a support organization or a tech docs department (or all three). These organizations specialize in building this kind of content. Rather than trying to build something that seems thin and fluffy, find existing content on your site that answers the question in an authoritative way. It might not be written with your target audience in mind. So you might need to build a wrapper around it, or translate it into common speech. But chances are, you have lots of content to answer the how questions. You just have to build experiences that make it easy for your audience to use the content.

If you build good answers for all the what, why and how questions mined from search queries, you will have covered the majority of the awareness and consideration phases of the buyers journey. And you will have content that addresses the needs of a large portion of your target audiences.

Other questions can come from other interaction points on your site, such as your site search or chat bots. How much does it cost? What are the terms? etc. A lot of companies start by building the content that answers the questions their sales people answer every day. Chances are, you already answer these questions as part of your product content anyway.

James Mathewson is IBM's Distinguished Technical Marketer for search. He has 20 years of experience in web editorial, content strategy, and SEO for large and small companies. A frequent speaker, lecturer and blogger, James has published more than 1600 articles and two books on how web technology and user experience change the nature of effective content. James has two advanced degrees on related subjects from the University of Minnesota.